Overview

Dataset statistics

Number of variables14
Number of observations4372
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory478.3 KiB
Average record size in memory112.0 B

Variable types

NUM14

Warnings

201103 is highly correlated with 201012 and 7 other fieldsHigh correlation
201012 is highly correlated with 201103 and 4 other fieldsHigh correlation
201101 is highly correlated with 201103 and 1 other fieldsHigh correlation
201102 is highly correlated with 201103 and 1 other fieldsHigh correlation
201104 is highly correlated with 201012 and 4 other fieldsHigh correlation
201105 is highly correlated with 201102 and 2 other fieldsHigh correlation
201106 is highly correlated with 201105 and 1 other fieldsHigh correlation
201107 is highly correlated with 201012 and 4 other fieldsHigh correlation
201110 is highly correlated with 201106High correlation
201111 is highly correlated with 201012 and 5 other fieldsHigh correlation
201112 is highly correlated with 201012 and 4 other fieldsHigh correlation
201012 is highly skewed (γ1 = 61.08787263) Skewed
201101 is highly skewed (γ1 = 45.79007171) Skewed
201102 is highly skewed (γ1 = 44.60023013) Skewed
201103 is highly skewed (γ1 = 52.70101885) Skewed
201104 is highly skewed (γ1 = 55.3589938) Skewed
201105 is highly skewed (γ1 = 41.76877766) Skewed
201106 is highly skewed (γ1 = 39.09144293) Skewed
201107 is highly skewed (γ1 = 51.71521442) Skewed
201108 is highly skewed (γ1 = 35.03313489) Skewed
201109 is highly skewed (γ1 = 33.35022294) Skewed
201110 is highly skewed (γ1 = 36.05642898) Skewed
201111 is highly skewed (γ1 = 62.12608601) Skewed
201112 is highly skewed (γ1 = 56.96227946) Skewed
CustomerID has unique values Unique
201012 has 3423 (78.3%) zeros Zeros
201101 has 3591 (82.1%) zeros Zeros
201102 has 3574 (81.7%) zeros Zeros
201103 has 3354 (76.7%) zeros Zeros
201104 has 3474 (79.5%) zeros Zeros
201105 has 3294 (75.3%) zeros Zeros
201106 has 3323 (76.0%) zeros Zeros
201107 has 3381 (77.3%) zeros Zeros
201108 has 3391 (77.6%) zeros Zeros
201109 has 3073 (70.3%) zeros Zeros
201110 has 2952 (67.5%) zeros Zeros
201111 has 2666 (61.0%) zeros Zeros
201112 has 3689 (84.4%) zeros Zeros

Reproduction

Analysis started2022-11-02 07:44:58.314098
Analysis finished2022-11-02 07:45:18.653955
Duration20.34 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CustomerID
Real number (ℝ≥0)

UNIQUE

Distinct4372
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15299.67772
Minimum12346
Maximum18287
Zeros0
Zeros (%)0.0%
Memory size34.2 KiB
2022-11-02T02:45:18.745967image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12613.55
Q113812.75
median15300.5
Q316778.25
95-th percentile17984.45
Maximum18287
Range5941
Interquartile range (IQR)2965.5

Descriptive statistics

Standard deviation1722.390705
Coefficient of variation (CV)0.1125769272
Kurtosis-1.195793327
Mean15299.67772
Median Absolute Deviation (MAD)1483.5
Skewness0.0009180495309
Sum66890191
Variance2966629.742
MonotocityStrictly increasing
2022-11-02T02:45:18.841266image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
123461< 0.1%
 
162821< 0.1%
 
162951< 0.1%
 
162931< 0.1%
 
162921< 0.1%
 
162871< 0.1%
 
162841< 0.1%
 
162831< 0.1%
 
162811< 0.1%
 
162221< 0.1%
 
Other values (4362)436299.8%
 
ValueCountFrequency (%) 
123461< 0.1%
 
123471< 0.1%
 
123481< 0.1%
 
123491< 0.1%
 
123501< 0.1%
 
ValueCountFrequency (%) 
182871< 0.1%
 
182831< 0.1%
 
182821< 0.1%
 
182811< 0.1%
 
182801< 0.1%
 

201012
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct931
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.3076441
Minimum-1192.2
Maximum194353
Zeros3423
Zeros (%)78.3%
Memory size34.2 KiB
2022-11-02T02:45:18.975343image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1192.2
5-th percentile0
Q10
median0
Q30
95-th percentile571.621
Maximum194353
Range195545.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3020.678231
Coefficient of variation (CV)17.63306154
Kurtosis3911.443308
Mean171.3076441
Median Absolute Deviation (MAD)0
Skewness61.08787263
Sum748957.02
Variance9124496.977
MonotocityNot monotonic
2022-11-02T02:45:19.089340image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0342378.3%
 
326.430.1%
 
5102< 0.1%
 
-1.252< 0.1%
 
112.62< 0.1%
 
254.42< 0.1%
 
-172< 0.1%
 
-2.552< 0.1%
 
1022< 0.1%
 
156.652< 0.1%
 
Other values (921)93021.3%
 
ValueCountFrequency (%) 
-1192.21< 0.1%
 
-583.681< 0.1%
 
-295.091< 0.1%
 
-238.21< 0.1%
 
-227.441< 0.1%
 
ValueCountFrequency (%) 
1943531< 0.1%
 
27834.611< 0.1%
 
19950.661< 0.1%
 
13112.521< 0.1%
 
8591.881< 0.1%
 

201101
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct772
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.0878911
Minimum-1241.43
Maximum84925.88
Zeros3591
Zeros (%)82.1%
Memory size34.2 KiB
2022-11-02T02:45:19.211536image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1241.43
5-th percentile0
Q10
median0
Q30
95-th percentile538.3635
Maximum84925.88
Range86167.31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1483.319569
Coefficient of variation (CV)11.5804824
Kurtosis2489.296707
Mean128.0878911
Median Absolute Deviation (MAD)0
Skewness45.79007171
Sum560000.26
Variance2200236.944
MonotocityNot monotonic
2022-11-02T02:45:19.323044image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0359182.1%
 
16530.1%
 
-2.952< 0.1%
 
556.462< 0.1%
 
-5.12< 0.1%
 
-30.62< 0.1%
 
681.052< 0.1%
 
179.12< 0.1%
 
69.62< 0.1%
 
-32.852< 0.1%
 
Other values (762)76217.4%
 
ValueCountFrequency (%) 
-1241.431< 0.1%
 
-11261< 0.1%
 
-855.761< 0.1%
 
-419.41< 0.1%
 
-197.11< 0.1%
 
ValueCountFrequency (%) 
84925.881< 0.1%
 
26476.681< 0.1%
 
22998.41< 0.1%
 
18620.21< 0.1%
 
16774.721< 0.1%
 

201102
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct784
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.9210087
Minimum-1132.08
Maximum61516.5
Zeros3574
Zeros (%)81.7%
Memory size34.2 KiB
2022-11-02T02:45:19.442671image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1132.08
5-th percentile0
Q10
median0
Q30
95-th percentile510.013
Maximum61516.5
Range62648.58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1084.902947
Coefficient of variation (CV)9.523291264
Kurtosis2400.912897
Mean113.9210087
Median Absolute Deviation (MAD)0
Skewness44.60023013
Sum498062.65
Variance1177014.404
MonotocityNot monotonic
2022-11-02T02:45:19.554197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0357481.7%
 
16540.1%
 
102.92< 0.1%
 
209.252< 0.1%
 
3032< 0.1%
 
152< 0.1%
 
-2.952< 0.1%
 
337.22< 0.1%
 
142.42< 0.1%
 
299.752< 0.1%
 
Other values (774)77817.8%
 
ValueCountFrequency (%) 
-1132.081< 0.1%
 
-331.51< 0.1%
 
-186.351< 0.1%
 
-152.641< 0.1%
 
-102.581< 0.1%
 
ValueCountFrequency (%) 
61516.51< 0.1%
 
22752.461< 0.1%
 
14022.921< 0.1%
 
10535.481< 0.1%
 
7709.591< 0.1%
 

201103
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1004
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.2824977
Minimum-555.9
Maximum103302.47
Zeros3354
Zeros (%)76.7%
Memory size34.2 KiB
2022-11-02T02:45:19.675651image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-555.9
5-th percentile0
Q10
median0
Q30
95-th percentile626.4975
Maximum103302.47
Range103858.37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1694.180808
Coefficient of variation (CV)10.84050251
Kurtosis3154.731748
Mean156.2824977
Median Absolute Deviation (MAD)0
Skewness52.70101885
Sum683267.08
Variance2870248.611
MonotocityNot monotonic
2022-11-02T02:45:19.790123image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0335476.7%
 
6002< 0.1%
 
-2.952< 0.1%
 
251.522< 0.1%
 
374.642< 0.1%
 
183.62< 0.1%
 
2892< 0.1%
 
251.562< 0.1%
 
141.412< 0.1%
 
307.52< 0.1%
 
Other values (994)100022.9%
 
ValueCountFrequency (%) 
-555.91< 0.1%
 
-195.51< 0.1%
 
-76.31< 0.1%
 
-60.351< 0.1%
 
-53.11< 0.1%
 
ValueCountFrequency (%) 
103302.471< 0.1%
 
21462.41< 0.1%
 
16558.141< 0.1%
 
13500.51< 0.1%
 
12992.41< 0.1%
 

201104
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct885
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.8104119
Minimum-1591.2
Maximum67159.27
Zeros3474
Zeros (%)79.5%
Memory size34.2 KiB
2022-11-02T02:45:19.901839image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-1591.2
5-th percentile0
Q10
median0
Q30
95-th percentile512.7645
Maximum67159.27
Range68750.47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1078.767912
Coefficient of variation (CV)9.562662643
Kurtosis3417.768755
Mean112.8104119
Median Absolute Deviation (MAD)0
Skewness55.3589938
Sum493207.121
Variance1163740.208
MonotocityNot monotonic
2022-11-02T02:45:20.013424image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0347479.5%
 
300.252< 0.1%
 
-7.952< 0.1%
 
169.52< 0.1%
 
-4.952< 0.1%
 
8162< 0.1%
 
6422< 0.1%
 
-12.752< 0.1%
 
244.52< 0.1%
 
-29.852< 0.1%
 
Other values (875)88020.1%
 
ValueCountFrequency (%) 
-1591.21< 0.1%
 
-1462.51< 0.1%
 
-155.521< 0.1%
 
-143.71< 0.1%
 
-131.411< 0.1%
 
ValueCountFrequency (%) 
67159.271< 0.1%
 
9656.851< 0.1%
 
7325.841< 0.1%
 
6367.21< 0.1%
 
4572.321< 0.1%
 

201105
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1063
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.446823
Minimum-3585.84
Maximum75082.43
Zeros3294
Zeros (%)75.3%
Memory size34.2 KiB
2022-11-02T02:45:20.133200image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-3585.84
5-th percentile0
Q10
median0
Q30
95-th percentile686.5395
Maximum75082.43
Range78668.27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1361.078888
Coefficient of variation (CV)8.22668495
Kurtosis2159.444909
Mean165.446823
Median Absolute Deviation (MAD)0
Skewness41.76877766
Sum723333.51
Variance1852535.741
MonotocityNot monotonic
2022-11-02T02:45:20.247097image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0329475.3%
 
-7.530.1%
 
185.222< 0.1%
 
214.52< 0.1%
 
251.022< 0.1%
 
-9.92< 0.1%
 
1032.462< 0.1%
 
281.662< 0.1%
 
1042< 0.1%
 
1792< 0.1%
 
Other values (1053)105924.2%
 
ValueCountFrequency (%) 
-3585.841< 0.1%
 
-262.81< 0.1%
 
-103.31< 0.1%
 
-41.31< 0.1%
 
-271< 0.1%
 
ValueCountFrequency (%) 
75082.431< 0.1%
 
28408.141< 0.1%
 
18165.741< 0.1%
 
18025.681< 0.1%
 
12691.161< 0.1%
 

201106
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1036
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.0793962
Minimum-608.84
Maximum83109.96
Zeros3323
Zeros (%)76.0%
Memory size34.2 KiB
2022-11-02T02:45:20.369933image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-608.84
5-th percentile0
Q10
median0
Q30
95-th percentile591.378
Maximum83109.96
Range83718.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1588.492908
Coefficient of variation (CV)10.04870303
Kurtosis1842.541565
Mean158.0793962
Median Absolute Deviation (MAD)0
Skewness39.09144293
Sum691123.12
Variance2523309.718
MonotocityNot monotonic
2022-11-02T02:45:20.481475image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0332376.0%
 
1352< 0.1%
 
134.12< 0.1%
 
1052< 0.1%
 
35.42< 0.1%
 
302.722< 0.1%
 
472.012< 0.1%
 
181.32< 0.1%
 
5002< 0.1%
 
158.852< 0.1%
 
Other values (1026)103123.6%
 
ValueCountFrequency (%) 
-608.841< 0.1%
 
-330.121< 0.1%
 
-209.51< 0.1%
 
-1951< 0.1%
 
-1671< 0.1%
 
ValueCountFrequency (%) 
83109.961< 0.1%
 
41959.441< 0.1%
 
25288.991< 0.1%
 
23426.811< 0.1%
 
20427.981< 0.1%
 

201107
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct976
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.8325963
Minimum-4287.63
Maximum107061.63
Zeros3381
Zeros (%)77.3%
Memory size34.2 KiB
2022-11-02T02:45:20.592936image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-4287.63
5-th percentile0
Q10
median0
Q30
95-th percentile637.343
Maximum107061.63
Range111349.26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1771.125212
Coefficient of variation (CV)11.36556314
Kurtosis3054.471917
Mean155.8325963
Median Absolute Deviation (MAD)0
Skewness51.71521442
Sum681300.111
Variance3136884.517
MonotocityNot monotonic
2022-11-02T02:45:20.704351image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0338177.3%
 
-8.530.1%
 
346.382< 0.1%
 
360.542< 0.1%
 
319.852< 0.1%
 
110.142< 0.1%
 
229.142< 0.1%
 
130.22< 0.1%
 
312.92< 0.1%
 
371.242< 0.1%
 
Other values (966)97222.2%
 
ValueCountFrequency (%) 
-4287.631< 0.1%
 
-1592.491< 0.1%
 
-1000.371< 0.1%
 
-717.231< 0.1%
 
-611.861< 0.1%
 
ValueCountFrequency (%) 
107061.631< 0.1%
 
26464.991< 0.1%
 
19889.161< 0.1%
 
13445.331< 0.1%
 
11590.581< 0.1%
 

201108
Real number (ℝ)

SKEWED
ZEROS

Distinct966
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.1483326
Minimum-485.14
Maximum66312.51
Zeros3391
Zeros (%)77.6%
Memory size34.2 KiB
2022-11-02T02:45:20.826127image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-485.14
5-th percentile0
Q10
median0
Q30
95-th percentile645.8615
Maximum66312.51
Range66797.65
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1352.922537
Coefficient of variation (CV)8.66434188
Kurtosis1508.356844
Mean156.1483326
Median Absolute Deviation (MAD)0
Skewness35.03313489
Sum682680.51
Variance1830399.392
MonotocityNot monotonic
2022-11-02T02:45:20.937512image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0339177.6%
 
-1530.1%
 
312.142< 0.1%
 
301.922< 0.1%
 
301.72< 0.1%
 
-172< 0.1%
 
-9.92< 0.1%
 
115.52< 0.1%
 
76.322< 0.1%
 
304.22< 0.1%
 
Other values (956)96222.0%
 
ValueCountFrequency (%) 
-485.141< 0.1%
 
-344.941< 0.1%
 
-220.471< 0.1%
 
-134.551< 0.1%
 
-1251< 0.1%
 
ValueCountFrequency (%) 
66312.511< 0.1%
 
39655.811< 0.1%
 
21880.441< 0.1%
 
21149.051< 0.1%
 
15952.381< 0.1%
 

201109
Real number (ℝ)

SKEWED
ZEROS

Distinct1288
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.2313866
Minimum-561.6
Maximum88588.23
Zeros3073
Zeros (%)70.3%
Memory size34.2 KiB
2022-11-02T02:45:21.261927image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-561.6
5-th percentile0
Q10
median0
Q3153.045
95-th percentile829.6875
Maximum88588.23
Range89149.83
Interquartile range (IQR)153.045

Descriptive statistics

Standard deviation2015.042361
Coefficient of variation (CV)8.639670631
Kurtosis1272.14234
Mean233.2313866
Median Absolute Deviation (MAD)0
Skewness33.35022294
Sum1019687.622
Variance4060395.715
MonotocityNot monotonic
2022-11-02T02:45:21.373572image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0307370.3%
 
-1.6530.1%
 
1304.042< 0.1%
 
-4.952< 0.1%
 
118.82< 0.1%
 
115.22< 0.1%
 
752< 0.1%
 
152< 0.1%
 
331.142< 0.1%
 
228.962< 0.1%
 
Other values (1278)128029.3%
 
ValueCountFrequency (%) 
-561.61< 0.1%
 
-213.681< 0.1%
 
-133.151< 0.1%
 
-115.431< 0.1%
 
-93.181< 0.1%
 
ValueCountFrequency (%) 
88588.231< 0.1%
 
70246.51< 0.1%
 
49622.181< 0.1%
 
26750.71< 0.1%
 
21012.171< 0.1%
 

201110
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1400
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.9004277
Minimum-983.87
Maximum96099.63
Zeros2952
Zeros (%)67.5%
Memory size34.2 KiB
2022-11-02T02:45:21.495329image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-983.87
5-th percentile0
Q10
median0
Q3188.5425
95-th percentile897.9025
Maximum96099.63
Range97083.5
Interquartile range (IQR)188.5425

Descriptive statistics

Standard deviation1919.974088
Coefficient of variation (CV)7.839815169
Kurtosis1596.73994
Mean244.9004277
Median Absolute Deviation (MAD)0
Skewness36.05642898
Sum1070704.67
Variance3686300.499
MonotocityNot monotonic
2022-11-02T02:45:21.607779image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0295267.5%
 
-3.7530.1%
 
118.830.1%
 
-1530.1%
 
18030.1%
 
158.42< 0.1%
 
300.762< 0.1%
 
313.42< 0.1%
 
616.82< 0.1%
 
240.552< 0.1%
 
Other values (1390)139832.0%
 
ValueCountFrequency (%) 
-983.871< 0.1%
 
-788.381< 0.1%
 
-468.321< 0.1%
 
-442.891< 0.1%
 
-134.961< 0.1%
 
ValueCountFrequency (%) 
96099.631< 0.1%
 
52681.271< 0.1%
 
39995.951< 0.1%
 
19180.91< 0.1%
 
17433.291< 0.1%
 

201111
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1672
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean334.3449794
Minimum-295.73
Maximum329494.22
Zeros2666
Zeros (%)61.0%
Memory size34.2 KiB
2022-11-02T02:45:21.729534image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-295.73
5-th percentile0
Q10
median0
Q3280.515
95-th percentile1013.73
Maximum329494.22
Range329789.95
Interquartile range (IQR)280.515

Descriptive statistics

Standard deviation5087.51706
Coefficient of variation (CV)15.21637044
Kurtosis4011.982885
Mean334.3449794
Median Absolute Deviation (MAD)0
Skewness62.12608601
Sum1461756.25
Variance25882829.84
MonotocityNot monotonic
2022-11-02T02:45:21.841180image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0266661.0%
 
178.2630.1%
 
244.862< 0.1%
 
292.342< 0.1%
 
-12.752< 0.1%
 
486.962< 0.1%
 
97.52< 0.1%
 
426.372< 0.1%
 
133.682< 0.1%
 
114.62< 0.1%
 
Other values (1662)168738.6%
 
ValueCountFrequency (%) 
-295.731< 0.1%
 
-207.221< 0.1%
 
-147.871< 0.1%
 
-136.851< 0.1%
 
-1331< 0.1%
 
ValueCountFrequency (%) 
329494.221< 0.1%
 
27837.451< 0.1%
 
25375.411< 0.1%
 
24434.451< 0.1%
 
22536.211< 0.1%
 

201112
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct674
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.19213403
Minimum-436.2
Maximum91161.63
Zeros3689
Zeros (%)84.4%
Memory size34.2 KiB
2022-11-02T02:45:21.960873image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-436.2
5-th percentile0
Q10
median0
Q30
95-th percentile406.987
Maximum91161.63
Range91597.83
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1452.168439
Coefficient of variation (CV)14.63995561
Kurtosis3543.071957
Mean99.19213403
Median Absolute Deviation (MAD)0
Skewness56.96227946
Sum433668.01
Variance2108793.176
MonotocityNot monotonic
2022-11-02T02:45:22.072517image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0368984.4%
 
-8.330.1%
 
2322< 0.1%
 
442.92< 0.1%
 
215.252< 0.1%
 
264.912< 0.1%
 
128.762< 0.1%
 
152< 0.1%
 
-1.652< 0.1%
 
164.682< 0.1%
 
Other values (664)66415.2%
 
ValueCountFrequency (%) 
-436.21< 0.1%
 
-381.951< 0.1%
 
-3361< 0.1%
 
-172.891< 0.1%
 
-1251< 0.1%
 
ValueCountFrequency (%) 
91161.631< 0.1%
 
12393.71< 0.1%
 
11728.021< 0.1%
 
11485.541< 0.1%
 
7835.541< 0.1%
 

Interactions

2022-11-02T02:44:58.914695image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:44:59.030271image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:44:59.135529image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:44:59.240268image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:44:59.343678image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:44:59.437695image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:44:59.540962image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:44:59.643697image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:44:59.737040image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:44:59.973336image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.067811image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.159861image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.254330image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.347646image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.441153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.543084image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.644909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.779242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.887641image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:00.993040image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:01.116125image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:01.220410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:01.324604image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:01.439325image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:01.543674image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:01.667971image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:01.787555image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:02.118319image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:02.231503image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:02.344234image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:02.444896image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:02.544950image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:02.645087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:02.738909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:02.821127image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:02.998519image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:03.091133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:03.174083image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:03.266437image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:03.348003image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:03.440713image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:03.632874image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:03.818094image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:03.911069image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:04.011904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:04.106460image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:04.198995image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:04.300853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:04.393654image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:04.485976image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:04.577646image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:04.852659image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:05.509447image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:05.611050image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:05.702839image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:05.793939image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:05.883185image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:05.976598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:06.067994image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:06.178752image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:06.288338image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:06.484450image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:06.585904image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:06.685194image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:06.778693image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:06.880742image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:06.972169image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:07.073730image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:07.953387image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:08.048762image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:08.152650image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:08.255390image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:08.346262image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:08.552877image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:09.642722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-11-02T02:45:15.043643image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:15.135051image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:15.226353image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:15.315676image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:15.409499image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:15.490913image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:15.592445image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:15.874913image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:15.966167image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.077909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.178733image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.280146image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.371570image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.462913image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.564324image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.663663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.758097image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.849491image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:16.948930image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.042378image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.132156image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.225720image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.307538image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.398785image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.479923image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.571339image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.662588image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.753910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.835077image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:17.927170image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:18.018573image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:18.099735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:18.199276image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-11-02T02:45:22.165971image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T02:45:22.321513image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T02:45:22.470713image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T02:45:22.622833image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T02:45:18.363059image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-11-02T02:45:18.564057image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Sample

First rows

CustomerID201012201101201102201103201104201105201106201107201108201109201110201111201112
0123460.000.000.00.000.000.00.000.00.000.00.000.000.00
112347711.79475.390.00.00636.250.0382.520.0584.910.01294.320.00224.82
212348892.80227.440.00.00367.000.00.000.00.00310.00.000.000.00
3123490.000.000.00.000.000.00.000.00.000.00.001757.550.00
4123500.000.00334.40.000.000.00.000.00.000.00.000.000.00
5123520.000.00296.5304.680.000.00.000.00.00632.50.00311.730.00
6123530.000.000.00.000.0089.00.000.00.000.00.000.000.00
7123540.000.000.00.001079.400.00.000.00.000.00.000.000.00
8123550.000.000.00.000.00459.40.000.00.000.00.000.000.00
9123560.002271.620.00.00481.460.00.000.00.000.00.0058.350.00

Last rows

CustomerID201012201101201102201103201104201105201106201107201108201109201110201111201112
4362182730.00.000.051.00.000.000.000.000.00102.00.000.0051.00
4363182740.00.000.00.00.000.000.000.000.000.00.000.000.00
4364182760.00.000.00.00.000.000.000.000.000.0335.86-12.500.00
4365182770.0-12.750.00.00.000.000.000.000.000.0110.380.000.00
4366182780.00.000.00.00.000.000.000.000.00173.90.000.000.00
4367182800.00.000.0180.60.000.000.000.000.000.00.000.000.00
4368182810.00.000.00.00.000.0080.820.000.000.00.000.000.00
4369182820.00.000.00.00.000.000.000.0098.760.00.000.0077.84
4370182830.0215.00102.90.0117.6899.47307.53143.190.00134.9114.65651.56208.00
4371182870.00.000.00.00.00765.280.000.000.000.01072.000.000.00